Drawing graphs

Our data

• To illustrate making graphs, we need some data.
• Data on 202 male and female athletes at the Australian Institute of Sport.
• Variables:
  • categorical: Sex of athlete, sport they play
  • quantitative: height (cm), weight (kg), lean body mass, red and white blood cell counts, haematocrit and haemoglobin (blood), ferritin concentration, body mass index, percent body fat.
• Values separated by tabs (which impacts reading in).

Packages for this section

library(tidyverse)

Reading data into R

• Use read_tsv (“tab-separated values”), like read_csv.
• Data in ais.txt:

my_url <- "http://ritsokiguess.site/datafiles/ais.txt"
athletes <- read_tsv(my_url)

The data (some)

athletes

ggplot

• R has a standard graphing procedure ggplot, that we use for all our graphs.
• Use in different ways to get precise graph we want.
• Let’s start with bar chart of the sports played by the athletes.

Bar chart

ggplot(athletes, aes(x = Sport)) + geom_bar()

Histogram of body mass index

ggplot(athletes, aes(x = BMI)) + geom_histogram(bins = 10)

Which sports are played by males and females?

Grouped bar chart:

ggplot(athletes, aes(x = Sport, fill = Sex)) +
  geom_bar(position = "dodge")

BMI by gender

ggplot(athletes, aes(x = Sex, y = BMI)) + geom_boxplot() 

Height vs. weight

Scatterplot:

ggplot(athletes, aes(x = Ht, y = Wt)) + geom_point()

With regression line

ggplot(athletes, aes(x = Ht, y = Wt)) +
  geom_point() + geom_smooth(method = "lm")

BMI by sport and gender

ggplot(athletes, aes(y = Sport, x = BMI, fill = Sex)) +
  geom_boxplot()

A variation that is colour-blind-friendly:

library(RColorBrewer)
ggplot(athletes, aes(colour = Sport, y = BMI, x = Sex)) +
  geom_boxplot() + scale_color_brewer(palette = "Set3")

A variation that uses fill instead of colour:

ggplot(athletes, aes(x = Sport, y = BMI, fill = Sex)) +
  geom_boxplot()

Height and weight by gender

ggplot(athletes, aes(x = Ht, y = Wt, colour = Sex)) +
  geom_point()

Height by weight by gender for each sport, with facets

ggplot(athletes, aes(x = Ht, y = Wt, colour = Sex)) +
  geom_point() + facet_wrap(~Sport)

Filling each facet

Default uses same scale for each facet. To use different scales for each facet, this:

ggplot(athletes, aes(x = Ht, y = Wt, colour = Sex)) +
  geom_point() + facet_wrap(~Sport, scales = "free")

Another view of height vs weight

ggplot(athletes, aes(x = Ht, y = Wt)) +
  geom_point() + facet_wrap(~ Sex)